International Journal of Communication Systems最新文献

The nonlinear power amplifier (NPA) in a cognitive radio (CR) network can degrade sensing performance. While spectrum sensing primarily focuses on the receiver, modeling the transmitter's NPA is crucial to understand its impact on detection. This paper investigates spectrum sensing performance in CR networks assisted by intelligent reconfigurable surfaces (IRS), considering the impact of NPA distortions. A novel multiple-input single-output (MISO) IRS-aided CR system, termed MICR, is proposed. The sensing performance of the proposed MICR system is analyzed in terms of the probabilities of false alarm and detection, considering the effect of IRS elements. The results are presented using receiver operating characteristic (ROC) curves. Furthermore, the impact of various system parameters such as the number of samples, number of IRS elements, IRS position, number of transmitting antennas, and phase shifts is analyzed. Additionally, the effects of line-of-sight (LoS) and non–line-of-sight (NLoS) propagation are evaluated. The ROC performance using different signal detectors is analyzed. The impact of interference, IRS overhead, and sensing energy on the proposed MICR system is also investigated. Extensive simulations demonstrate that IRS significantly improves the sensing performance of the CR network, both with and without NPA.

Performance issues with spectrum sensing are caused by multipath fading and shadowing, and obstacle blocking. This paper presents a novel Hybrid spectrum sensing framework that cooperatively combines energy detection (ED), matched filter detection (MFD), and cyclostationary feature detection (CFD) to detection reliability in cognitive radio networks. To mitigate fading and shadowing effects, cooperative spectrum sensing (CSS) is incorporated, leveraging multiple secondary users (SUs) for spatial diversity, achieving P_d = 0.80 at SNR = −20 dB using the OR fusion rule The key novelty lies in a dynamic switching mechanism that adaptively selects among ED, MFD, and CFD based on real-time signal and PU information availability, enabling operation under full, limited, or absent PU knowledge. Monte Carlo simulations in the low SNR region (−20 to −5 dB) demonstrate that the proposed hybrid detector achieves P_d values of 1, 0.98, and 0.95 for CFD, ED, and MFD, respectively, at P_f = 0.1. Moreover, it achieves energy efficiencies of 1.86 bits/J (non-CSS) and 2.05 bits/J (CSS), emphasizing the importance of energy-efficient spectrum access in power-constrained CR-IoT applications. The framework also minimizes average detection delay to 1.16 ms, ensuring faster PU identification. Owing to its adaptability and energy-aware operation, the proposed method is suitable for TV white spaces, CR-IoT, 5G-and-beyond networks, VANETs, and disaster-recovery communications.

Efficient resource allocation is vital for ensuring quality of service (QoS) in Long-Term Evolution (LTE) networks, especially for call admission control (CAC). This study introduces a multilevel bandwidth (BW) allocation model that dynamically assigns BW based on call requirements and computes the minimum necessary BW when availability is limited. Calls are classified as new calls (NC) or handoff calls (HC) based on network conditions. To enhance CAC decision-making, a cascade feedforward network (Cascade FFN), integration of a cascade neuro-fuzzy network and a deep feedforward neural network (DFNN), is employed. The proposed model achieves high BW utilization of 0.911, power efficiency of 65.098 dBm, and mean throughput of 516,830.584 bps. Also, it minimizes call blocking probabilities for NC, which is 0.591, and for HC, which is 0.605; the call dropping probabilities for NC, which is 0.606, and for HC, which is 0.592; the mean delay of 0.066 s; and the number of dropped users of 820.620. These results demonstrate significant improvements in LTE performance through intelligent BW allocation and CAC.

The efficient path selection of data in wireless sensor networks is vital for enhancing total system performance. Traditional path selection protocols encounter challenges related to frequent node movements, optimization of energy efficiency, and the nature of network environments. To tackle these challenges, an innovative methodology termed Deep reinforcement arctic puffin federated stochastic gradient learning (DRAFSGL) integrates federated learning and puffin optimization for energy-efficient multipath routing. Multiview deep embedded clustering (MDEC), enhanced by coyote and badger optimization into efficient clusters to reduce routing complexity. To ensure privacy, quantum-resistant homomorphic encryption (QRHE) enables secure computation without safeguarding against quantum threats. The data monitoring system applies game theory to optimize the behavior of agents involved in monitoring activities in the system. The suggested approach has exhibited extraordinary evaluation, attaining a peak accuracy of 99.7% alongside a precision of 98.9%, a specificity of 98.4%, a delay of 18 ms, a throughput of 87.9 Kbps, and a remarkable recall rate of 98.9%, as well as an F1-score when compared to existing methods. Overall, DRAFSGL methodology improves adaptive, secure, and energy-efficient multipath routing through the combination of federated learning and arctic puffin optimization. Meanwhile, the MDEC technique with improved coyote and badger optimization (ICBO) simplifies routing complexity and QRHE guarantees quantum-resistant, secure data transmission, effectively addressing the shortcomings of current techniques in dynamic 6G wireless sensor network (WSN)-based Internet of Things (IoT) systems.

The tactile internet of things (TIoT) demands ultralow latency, high reliability, and dynamic resource allocation for real-time human–machine interactions. Traditional network slicing methods (e.g., peak or average-based provisioning) fail to adapt to fluctuating haptic signals, causing inefficiencies like overprovisioning or underprovisioning. This paper proposes an intelligent in-content-aware elastic resource allocation framework for 6G-enabled TIoT, combining federated learning (FL) and dynamic network slicing to optimize resource allocation. FL enables distributed prediction of resource demands at tactile devices, preserving privacy and reducing bandwidth overhead. The framework dynamically adjusts network slices using an AI-driven close-loop feedback mechanism, improving elasticity. Building on prior FL-based architecture, this study further refines dynamic allocation for fluctuating tactile signals. Experiments show that the framework outperforms static methods, reducing packet loss by 17%, improving resource efficiency by 30%, and enhancing fairness under varying workloads. Key innovations include modeling TIoT signals with generalized Pareto–Poisson models, an end-to-end semantic orchestration workflow, and empirical validation of resilience to asynchronism and service level agreement (SLA) violations. The results highlight its potential to boost quality of service (QoS) and cost-efficiency in next-gen tactile applications.

Wireless sensor networks (WSNs) essentially aid in numerous medical and networking applications. However, they face challenges, such as the availability of limited energy resources and the need to satisfy varying service quality requirements with efficient data transmission. Most of the traditional methods used to deal with these challenges often consume significant energy, limiting the operational lifespan of the sensor nodes. Hence, our research attempts to overcome the limitations in the existing WSN solutions by proposing a cross-layer design (CLD) with a novel algorithmic optimization, focusing more on energy efficiency and communication optimization. The novel beluga whale–adapted Namib beetle optimization (BWANBO) presented here effectively addresses the complex optimization challenges in WSNs by balancing multiple conflicting objectives, such as maximizing data rates, minimizing energy consumption, and ensuring reliable communication under varying network conditions. Additionally, k-means clustering is employed to form the clusters of nodes, and BWANBO uses parameters, like node energy, quality of service (QoS), improved trust, and security, to select the cluster head (CH). Integrating the novel virtual multiple-input multiple-output (MIMO) technology with the optimized CLD framework, this research simultaneously aims to reduce the transmission energy, boost the data rates, and improve the communication among diverse protocol layers. The resultant expression is a significant extension in the operational lifespan of the sensor nodes. Finally, the outcomes reveal that the BWANBO algorithm effectively optimizes the bit error rate (BER) performance, under the consideration of end-to-end (ETE) throughput and latency.

Joint sensing and communication (JSAC) technology is anticipated to enable autonomous driving and Extended Reality (XR) in future Wireless Communication Systems (WCS). Pilot signals in wireless communications have excellent passive recognition, anti-noise and autocorrelation capabilities, which lead to suitability for radar sensing. In this paper, Parameter Estimation using multiple signal classification algorithm for JSAC system (JSAC–MUSIC–PET–OCDM) is proposed. Here, the Multiple Signal Classification (MUSIC) algorithm evaluates radar parameters, demonstrating its effectiveness in simultaneous radar sensing, communication, and positioning. This generates a Cramer–Rao Lower Bound (CRLB) for range and velocity estimation in MUSIC-based positioning, thereby providing a theoretical framework for performance evaluation. The results show that the OCDM-MUSIC-based JSAC system significantly improves the efficiency of radar parameter estimation and the overall system performance. The proposed JSAC-MUSIC-PET-OCDM method is implemented in Python. The JSAC-MUSIC-PET-OCDM attains 28.96%, 33.21%, and 23.89% higher SNR; 22.87%, 31.36%, and 20.34% lower RMSE when compared with existing methods: Fifth generation positioning reference signal-dependent sensing: a sensing reference signal method for combined sensing and communication system (PRS-FFT-JSAC-OFDM), Radar sensing via OTFS signaling (RS-PA-OTFS), and evaluation technique of sensing parameters depending upon Orthogonal Time Frequency Space (MFF-ISAC-OTFS) methods respectively.

Cognitive radio (CR) is a new technology that aims to make better use of the radio spectrum. Its core component is spectrum sensing, which is difficult to accomplish precisely because many factors affect detection performance. Numerous spectrum sensing techniques, including the cyclic stationary detection algorithm, matching filter detection algorithm, energy detection algorithm, and others have been presented recently; these algorithms, though, depend on specific previous knowledge and are model-driven. Inaccurate model assumptions or difficult-to-obtain prior knowledge will impair the algorithms' detection performance. A novel method of achieving spectrum sensing is made possible by the advancement of completely learning alongside neural network capabilities. In this paper, spectrum sensing in cooperative cognitive radio networks utilizing binarized simplicial convolutional neural network performance in dynamic environments (SS-CCRN-BSCNN-DE) is proposed. Initially, the input signals are gathered from the GSM-900 spectrum dataset. Then, the data are preprocessed utilizing reverse lognormal Kalman filtering (RLKF) to normalize the gathered signals. Then, the preprocessed data are fed into feature extraction using quadratic phase S-transform (QPST) to extract the hidden spatial features. Then, the extracted features are fed into binarized simplicial convolutional neural network (BSCNN) for detecting the spectrum sensing in the cognitive radio system. The multiobjective thermal exchange optimization (MOTEO) is implemented to optimize the hyperparameters of BSCNN. The proposed method is implemented and its performance is evaluated under some metrics, like accuracy, signal-to-noise ratio (SNR), and root mean square error (RMSE), as well as computational time. The performance of the SS-CCRN-BSCNN-DE approach attains 16.21%, 17.18%, and 24.44% higher accuracy; 21.25%, 17.25%, and 13.32% lower RMSE; 11.17%, 19.15%, and 23.14% lower SNR when compared with existing methods: cooperative spectrum sensing depending on convolutional neural network (CNN) in cognitive radio scheme (CSS-CNN-CRS), improved spectrum prediction method for cognitive radio networks utilizing artificial neural network (ISP-CRN-ANN), and recurrent neural network based spectrum sensing cognitive radio (RNN-SS-CR), respectively.

Internet of Things (IoT) has been diversified smart networks of connected things consisting of users, smart devices, and interwoven equipment that facilitate communication using wired and wireless mechanisms. The devices in IoT networks transfer data among each other to provide amenities to mankind. These networks have been in hot demand and use in the recent past and have scaled up to a large extent. Fog-based IoTs have been maneuvered as a furtherance to cloud computing by accomplishing the processing of services faster and closer to end users. However, the rapid increase in the demand for services has raised various concerns like security and privacy in fog-based IoTs, and therefore, the identification and removal of malicious devices have become a prominent challenge. To conquer this, a secure method called TRUFOG is presented in this article. TRUFOG is a task-based dynamic trust mechanism. The proposed approach utilizes the following three major parameters: task success, task pending, and task failure ratio. Along with this recommendation credibility is used to estimate the trust of the fog node and average energy consumption is also tested. The security resistance of the TRUFOG mechanism is tested against black hole, gray hole, and energy consumption attacks. The proposed mechanism is extensively evaluated and compared with existing methods like TCF, TBMSC, and TRDTM in terms of accuracy, delivery ratio, energy consumption, and other metrics. The results have proven that TRUFOG has effectively recognized the malicious node in the network with a detection rate of 95.2%. The packet delivery ratio for TRUFOG under attack was found to be 4.5% better than TCF, 6.25% better than TBMSC, and 4.66% better than TRDTM. Furthermore, the average energy consumption of the TRUFOG method was found to be 13.6% lesser than TCF, 30.7% lesser than TBMSC, and 49.9% lesser than the TRDTM model.

DoS attacks pose significant threats to wireless sensor networks (WSNs) by disrupting regular network availability. The existing systems face limitations such as limited power, storage, bandwidth, and processing capabilities, making them particularly vulnerable to security risks. Despite these constraints, an effective intrusion detection system (IDS) is essential for detecting such attacks. As denial-of-service (DoS) attacks become more frequent and sophisticated, the traditional intrusion detection systems are losing their effectiveness. To overcome these complications, Efficient Detection of Denial-of-Service Attacks in Wireless Sensor Networks using Binarized Simplicial Convolutional Neural Networks for Enhanced Security (ED-DoS-WSN-BSCNN) is proposed. The input data are collected from the WSN-DS dataset. The gathered data are given to the preprocessing stage with the help of the adaptive two-stage unscented Kalman filter (ATSUKF) for data cleaning, data transformation, and normalization. Then the preprocessed data are given to the classification stage by using the binarized simplicial convolutional neural network (BSCNN) for classifying DoS attacks, such as normal, blackhole, grayhole, flooded, and TDMA. Finally, the Arctic tern optimizer (ATO) algorithm is employed to enhance the BSCNN that categorizes the types of DoS attacks accurately. The performance metrics like accuracy, precision, recall, specificity, F1-score, computational time, and RoC are taken into account. The performance of the proposed technique is compared with other existing methods. The ED-DoS-WSN-BSCNN technique is implemented in Python. The proposed technique attains 4.05%, 7.52%, and 2.91% higher accuracy, 4.10%, 7.61%, and 5.14% higher precision, 7.46%, 6.92%, and 2.88% higher recall, and 1.06%, 1.75%, and 2.31% higher specificity compared with existing methods: performance analysis of deep learning for DoS attacks identification in wireless sensor network (CNN-DoS-WSN), detection of DoS attack in wireless sensor networks: a lightweight machine learning approach (KNN-DoS-WSN), and extended evaluation on machine learning approach for DoS detection in Wireless Sensor Networks (RT-DoS-WSN), respectively.